



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]						)->	Dict:
    A__					=       os.path.join(args.tf_model_dir			,							'''parameters.json'''						)
    A__					=       json.loads(open(a_						).read()						)
    if not params:
        raise ValueError(
            f"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."						)
    if not args.output.endswith('''.pt'''						):
        A__					=       args.output + '''.pt'''
    A__					=       OrderedDict()
    with tf.device('''/CPU:0'''						):
        A__					=       tf.train.load_checkpoint(args.tf_model_dir						)
        A__					=       reader.get_variable_to_shape_map()
        for key_name in shapes.keys():
            A__					=       reader.get_tensor(a_						).astype(np.floataa						)
            if key_name.endswith('''/adam_m'''						) or key_name.endswith('''/adam_v'''						):
                continue
            if key_name.startswith('''pasts/'''						):
                if key_name.startswith('''pasts/mlp'''						):
                    A__					=       int(key_name[9]						)
                elif key_name.startswith('''pasts/out'''						):
                    A__					=       8
                A__					=       '''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                A__					=       vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                A__					=       torch.tensor(a_						)
            elif key_name.startswith('''model/moe'''						):
                A__					=       int(key_name[9:].split('''/'''						)[0]						)
                if key_name.endswith('''/switch_gating/kernel'''						):
                    A__					=       '''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
                    A__					=       vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                    A__					=       torch.tensor(a_						)
                elif key_name.endswith('''/softmlp/kernel'''						):
                    A__					=       '''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
                    A__					=       vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                    A__					=       torch.tensor(a_						)
                elif key_name.endswith('''/wo/kernel'''						) or key_name.endswith('''/wi/kernel'''						):
                    A__					=       key_name[-9:-7]
                    for i in range(1_6						):
                        A__					=       '''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
                        A__					=       (
                            vnp[i].transpose([1, 0]						).copy()
                        )  # In Mesh-Tensorflow, it is one array, so it is divided
                        A__					=       torch.tensor(a_						)
            elif key_name.startswith('''model/mlp'''						):
                A__					=       int(key_name[9:].split('''/'''						)[0]						)
                if key_name.endswith('''/p1/kernel'''						):
                    A__					=       '''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
                    A__					=       vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                    A__					=       torch.tensor(a_						)
                elif key_name.endswith('''/p1/bias'''						):
                    A__					=       '''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
                    A__					=       vnp.copy()  # same because it is one dimensional
                    A__					=       torch.tensor(a_						)
                elif key_name.endswith('''/p2/kernel'''						):
                    A__					=       '''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
                    A__					=       vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                    A__					=       torch.tensor(a_						)
                elif key_name.endswith('''/p2/bias'''						):
                    A__					=       '''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
                    A__					=       vnp.copy()  # same because it is one dimensional
                    A__					=       torch.tensor(a_						)
            elif key_name.startswith('''model/ln'''						):
                A__					=       int(key_name[8:].split('''/'''						)[0]						)
                if key_name.endswith('''/b'''						):
                    A__					=       '''model.blocks.%d.feed_forward.norm.bias''' % player
                    A__					=       vnp.copy()  # same because it is one dimensional
                    A__					=       torch.tensor(a_						)
                elif key_name.endswith('''/g'''						):
                    A__					=       '''model.blocks.%d.feed_forward.norm.weight''' % player
                    A__					=       vnp.copy()  # same because it is one dimensional
                    A__					=       torch.tensor(a_						)
            elif key_name.startswith('''model/att'''						):
                A__					=       int(key_name[9:].split('''/'''						)[0]						)
                if key_name.endswith('''/qkv/kernel'''						):
                    A__					=       vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                    A__					=       state[:, 0, :, :]
                    A__					=       state[:, 1, :, :]
                    A__					=       state[:, 2, :, :]
                    A__					=       (
                        state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]						)
                        .transpose([1, 0]						)
                        .copy()
                    )  # Mesh-Tensorflow is a diagonal matrix
                    A__					=       (
                        state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]						)
                        .transpose([1, 0]						)
                        .copy()
                    )  # Mesh-Tensorflow is a diagonal matrix
                    A__					=       (
                        state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]						)
                        .transpose([1, 0]						)
                        .copy()
                    )  # Mesh-Tensorflow is a diagonal matrix
                    A__					=       '''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
                    A__					=       torch.tensor(a_						)
                    A__					=       '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
                    A__					=       torch.tensor(a_						)
                    A__					=       '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
                    A__					=       torch.tensor(a_						)
                elif key_name.endswith('''/o/kernel'''						):
                    A__					=       '''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
                    A__					=       (
                        vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]						).transpose([1, 0]						).copy()
                    )  # Mesh-Tensorflow is a diagonal matrix
                    A__					=       torch.tensor(a_						)
            elif key_name.startswith('''model/an'''						):
                A__					=       int(key_name[8:].split('''/'''						)[0]						)
                if key_name.endswith('''/b'''						):
                    A__					=       '''model.blocks.%d.self_attn.norm.bias''' % player
                    A__					=       vnp.copy()  # same because it is one dimensional
                    A__					=       torch.tensor(a_						)
                elif key_name.endswith('''/g'''						):
                    A__					=       '''model.blocks.%d.self_attn.norm.weight''' % player
                    A__					=       vnp.copy()  # same because it is one dimensional
                    A__					=       torch.tensor(a_						)
            elif (
                key_name.startswith('''model/wte'''						)
                or key_name.startswith('''model/wpe'''						)
                or key_name.startswith('''model/ete'''						)
            ):
                A__					=       {'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
                    key_name[-3:]
                ]
                A__					=       '''model.%s.weight''' % nlayer
                A__					=       vnp.copy()  # same in embedded
                A__					=       torch.tensor(a_						)
                if key_name.startswith('''model/wte'''						):
                    A__					=       '''lm_head.weight'''
                    A__					=       vnp.copy()  # same in embedded
                    A__					=       torch.tensor(a_						)
            elif key_name.startswith('''model/wob'''						):
                A__					=       '''final_logits_bias'''
                A__					=       vnp.copy()  # same in embedded
                A__					=       state.reshape((1, -1)						)
                A__					=       torch.tensor(a_						)
            elif key_name == "model/dense/kernel":
                A__					=       '''model.last_project.weight'''
                A__					=       vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                A__					=       torch.tensor(a_						)
            elif key_name == "model/dense_1/bias":
                A__					=       '''model.last_project.bias'''
                A__					=       vnp.copy()  # same because it is one dimensional
                A__					=       torch.tensor(a_						)
    torch.save(a_			,							args.output						)


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser(
       description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
   )
   parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
   parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
   _lowercase    	=		parser.parse_args()
   convert_tf_gptsan_to_pt(args)






'''simple docstring'''


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase_					=   logging.getLogger()


def     SCREAMING_SNAKE_CASE							(   a_						:				Path					,       a_						:				list      ):
  __a								=       '\n'.join(a_      )
  Path(a_      ).open('w'      ).writelines(a_      )


UpperCAmelCase_					=   "patrickvonplaten/t5-tiny-random"
UpperCAmelCase_					=   "sshleifer/bart-tiny-random"
UpperCAmelCase_					=   "sshleifer/tiny-mbart"

UpperCAmelCase_					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class  __lowercase    (	__magic_name__			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()
     __a								=       [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
     _dump_articles(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       str(Path(self.get_auto_remove_tmp_dir()   ) / 'scores.json'   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       run_generate()
       assert Path(UpperCamelCase   ).exists()
       # os.remove(Path(output_file_name))







   def 		UpperCamelCase__      (						self   )					->     int:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([BART_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Union[str, Any]:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([T5_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()

     __a								=       {
         'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
         'de': [
             'Maschinelles Lernen ist großartig, oder?',
             'Ich esse gerne Bananen',
             'Morgen ist wieder ein toller Tag!',
         ],
     }

     __a								=       Path(self.get_auto_remove_tmp_dir()   )
     __a								=       str(tmp_dir / 'scores.json'   )
     __a								=       str(tmp_dir / 'val.target'   )
     _dump_articles(UpperCamelCase   ,				text['en']   )
     _dump_articles(UpperCamelCase   ,				text['de']   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {str(UpperCamelCase   )}\n            {str(UpperCamelCase   )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
     testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']   )

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       with CaptureStdout() as cs:
         run_search()
       __a								=       [' num_beams | length_penalty', model, 'Best score args']
       __a								=       ['Info']
       if "translation" in task:
         expected_strings.append('bleu'   )
       else:
         expected_strings.extend(UpperCamelCase   )
       for w in expected_strings:
         assert w in cs.out
       for w in un_expected_strings:
         assert w not in cs.out
       assert Path(UpperCamelCase   ).exists()
       os.remove(Path(UpperCamelCase   )   )



